gemseo_mlearning / regression

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gradient_boosting module

The gradient boosting for regression.

The gradient boosting model relies on the GradientBoostingRegressor class of the scikit-learn library.

class gemseo_mlearning.regression.gradient_boosting.GradientBoostingRegressor(data, transformer=None, input_names=None, output_names=None, n_estimators=100, **parameters)[source]

Bases: MLRegressionAlgo

Gradient boosting regression.

# noqa: D205 D212 D415

Parameters:

  • data (Dataset) – The learning dataset.

  • transformer (Mapping[str, TransformerType] | None) – The strategies to transform the variables. The values are instances of Transformer while the keys are the names of either the variables or the groups of variables, e.g. "inputs" or "outputs" in the case of the regression algorithms. If a group is specified, the Transformer will be applied to all the variables of this group. If IDENTITY, do not transform the variables.

  • input_names (Iterable[str]) – The names of the input variables. If None, consider all the input variables of the learning dataset.

  • output_names (Iterable[str]) – The names of the output variables. If None, consider all the output variables of the learning dataset.

  • n_estimators (int) –

    The number of boosting stages to perform.

    By default it is set to 100.

  • **parameters (Any) – The parameters of the machine learning algorithm.

Raises:

ValueError – When both the variable and the group it belongs to have a transformer.

LIBRARY: Final[str] = 'scikit-learn'

The name of the library of the wrapped machine learning algorithm.

SHORT_ALGO_NAME: ClassVar[str] = 'GradientBoostingRegressor'

The short name of the machine learning algorithm, often an acronym.

Typically used for composite names, e.g. f"{algo.SHORT_ALGO_NAME}_{dataset.name}" or f"{algo.SHORT_ALGO_NAME}_{discipline.name}".

algo: Any

The interfaced machine learning algorithm.

input_names: list[str]

The names of the input variables.

input_space_center: dict[str, ndarray]

The center of the input space.

learning_set: IODataset

The learning dataset.

output_names: list[str]

The names of the output variables.

parameters: dict[str, MLAlgoParameterType]

The parameters of the machine learning algorithm.

transformer: dict[str, Transformer]

The strategies to transform the variables, if any.

The values are instances of Transformer while the keys are the names of either the variables or the groups of variables, e.g. “inputs” or “outputs” in the case of the regression algorithms. If a group is specified, the Transformer will be applied to all the variables of this group.